import logging
import random
import numpy as np

from collections import defaultdict

from util.util_log import setup_logging
from util.util_csv import save_csv
from util.util_image import save_img, save_img_xyz, save_line_chart
from util.util_ris_pattern import point_2_phi_pattern, phase_2_bit, phase_2_pattern, phase_2_pattern_xyz_fft, \
    phase_2_pattern_xyz, eps

from multi_beam.multi_beam_PS import psm_beam_2, psm_beam_4, psm_beam_n

from util.util_analysis_plane import get_peaks, get_peak_3rd, get_peak_5th, get_peak_9th


# 配置日志，默认打印到控制台，也可以设置打印到文件
setup_logging()
# setup_logging(log_file="../../files/logs/log_multi_beam_PS.log")
# 获取日志记录器并记录日志
logger = logging.getLogger("[RIS-multi-beam-PS-1bit]")


# ============================================= 遗传算法 ===========================================
class RISGeneticAlgorithmPS():
    __bit_num = 0       # 比特数
    __beam_num = 0      # 波束数

    def __init__(self, bit_num, beam_num, population_size=50, num_generations=100, num_parents=10, mutation_rate=0.1):
        # 初始化阵列相关
        self.__bit_num = bit_num
        self.__beam_num = beam_num
        # 初始化遗传算法相关
        self.population_size = population_size
        self.num_generations = num_generations
        self.num_parents = num_parents
        self.mutation_rate = mutation_rate
        self.population = None
        self.best_individual = None
        self.best_fitness = None
        self.best_fitness_history = []  # 保存每一代的最佳适应度值
        self.best_individual_history = []  # 保存每一代的最佳适应度值的个体

    def __get_psll_2(self, patternBit_mix):
        psll = 0
        # pattern 转dB
        pattern_dbw = 20 * np.log10(np.abs(patternBit_mix) / np.max(np.max(np.abs(patternBit_mix))) + eps)
        # pattern找峰值
        peaks = get_peaks(pattern_dbw)
        # 找第三峰(小于3dB)作为 PSLL
        if len(peaks) > 2:
            peak_3rd = get_peak_3rd(peaks)
            if peak_3rd is not None:
                psll = peak_3rd[0]
        return psll

    def __get_psll_4(self, patternBit_mix):
        psll = 0
        # pattern 转dB
        pattern_dbw = 20 * np.log10(np.abs(patternBit_mix) / np.max(np.max(np.abs(patternBit_mix))) + eps)
        # pattern找峰值
        peaks = get_peaks(pattern_dbw)
        # 找第三峰(小于3dB)作为 PSLL
        if len(peaks) > 4:
            peak_5th = get_peak_5th(peaks)
            if peak_5th is not None:
                psll = peak_5th[0]
        return psll

    def __get_psll_8(self, patternBit_mix):
        psll = 0
        # pattern 转dB
        pattern_dbw = 20 * np.log10(np.abs(patternBit_mix) / np.max(np.max(np.abs(patternBit_mix))) + eps)
        # pattern找峰值
        peaks = get_peaks(pattern_dbw)
        # 找第三峰(小于3dB)作为 PSLL
        if len(peaks) > 8:
            peak_9th = get_peak_9th(peaks)
            if peak_9th is not None:
                psll = peak_9th[0]
        return psll

    def __get_psll(self, patternBit_mix):
        psll = 0
        if self.__beam_num == 2:
            psll = self.__get_psll_2(patternBit_mix)
        elif self.__beam_num == 4:
            psll = self.__get_psll_4(patternBit_mix)
        elif self.__beam_num == 8:
            psll = self.__get_psll_8(patternBit_mix)
        return psll

    def fitness(self, phase_mix):
        # 相位转换 X bit
        phaseBit_mix, phaseBitDeg_mix = phase_2_bit(phase_mix, self.__bit_num)
        # 计算phase_mix的方向图
        phaseBit_mix = np.deg2rad(phaseBitDeg_mix)
        # patternBit_mix = phase_2_pattern(phaseBit_mix)                # 公式法直接计算, 准确但速度太慢
        patternBit_mix, x, y = phase_2_pattern_xyz_fft(phaseBit_mix)    # FFT法计算, 快速
        # 计算码阵的最大副瓣
        psll = self.__get_psll(patternBit_mix)
        return psll

    # def fitness(self, cluster_init):
    #     list_sum = []
    #     for datas in cluster_init:
    #         sum = 0
    #         for data in datas:
    #             sum += data
    #         list_sum.append(sum)
    #     fit = 0
    #     for sum in list_sum:
    #         fit += sum ** 2
    #     return -fit

    def initialize_population(self, phase_mix_init):
        """初始化种群"""
        self.population = [phase_mix_init] * self.population_size

    def selection(self):
        """选择操作"""
        fitness_scores = [self.fitness(individual) for individual in self.population]
        sorted_indices = np.argsort(fitness_scores)  # 从低到高排序
        selected_parents = [self.population[i] for i in sorted_indices[:self.num_parents]]
        # 获取选中的父代个体对应的适应度值
        selected_fitness_scores = [fitness_scores[i] for i in sorted_indices[:self.num_parents]]
        return selected_parents, selected_fitness_scores

    def crossover(self, parents, offspring_size):
        """交叉操作"""
        offspring = []
        for _ in range(offspring_size):
            parent1, parent2 = random.sample(parents, 2)
            parent1 = np.array(parent1)
            parent2 = np.array(parent2)
            # 获取数组的形状
            shape = parent1.shape
            # 将 parent1 和 parent2 扁平化为一维数组
            flat_parent1 = parent1.flatten()
            flat_parent2 = parent2.flatten()
            # 随机选择一个切割位置
            cut_point = random.randint(0, len(flat_parent1))
            # 生成子代
            child = np.concatenate((flat_parent1[:cut_point], flat_parent2[cut_point:]))
            # 将子代重新塑形为原始的二维数组形状
            child = child.reshape(shape)
            child = child.tolist()
            #
            offspring.append(np.array(child))
        return offspring

    def mutation(self, offspring):
        """变异操作"""
        for individual in offspring:
            if random.random() < self.mutation_rate:
                # 获取数组的数量和长度
                num_clusters, array_length = individual.shape
                # 生成一个与 individual 形状相同的随机数数组
                random_values = np.random.uniform(-180, 180, (num_clusters, array_length))
                # 决定哪些元素需要变异
                mask = np.random.rand(num_clusters, array_length) < self.mutation_rate
                # 应用变异
                individual[mask] = random_values[mask]
        return offspring

    def run(self, phase_mix_init):
        logger.info("population_size=%d, num_generations=%d, num_parents=%d, mutation_rate=%d"
                    % (self.population_size, self.num_generations, self.num_parents, self.mutation_rate))
        # """初始化返回值"""
        self.best_fitness = self.fitness(phase_mix_init)
        self.best_individual = phase_mix_init
        self.best_fitness_history = []
        self.best_individual_history = []
        # """运行遗传算法 -- 初始化阶段"""
        self.initialize_population(phase_mix_init)
        # """运行遗传算法 -- 搜索阶段"""
        for generation in range(self.num_generations):
            # 选择操作
            selected_parents, selected_fitness_scores = self.selection()
            # 交换操作
            offspring = self.crossover(selected_parents, self.population_size - self.num_parents)
            # 变异操作
            offspring = self.mutation(offspring)
            # 计算后代的适应度值
            offspring_fitness_scores = [self.fitness(individual) for individual in offspring]
            # 更新种群
            self.population = selected_parents + offspring
            # 合并父代和后代的适应度值
            all_fitness_scores = selected_fitness_scores + offspring_fitness_scores
            #
            # 找到当前代的最佳个体
            this_best_index = np.argmax(all_fitness_scores)  # 找到最佳适应度值的索引
            if this_best_index < len(selected_parents):
                this_best_individual = selected_parents[this_best_index]  # 最佳个体来自父代
            else:
                this_best_individual = offspring[this_best_index - len(selected_parents)]  # 最佳个体来自后代
            this_best_fitness = all_fitness_scores[this_best_index]  # 最佳适应度值
            #
            # 更新最佳适应度
            if this_best_fitness < self.best_fitness:
                self.best_fitness = this_best_fitness
                self.best_individual = this_best_individual
            # 记录最佳适应度曲线
            self.best_fitness_history.append(self.best_fitness)
            self.best_individual_history.append(self.best_individual)
            #
            # logger.info("generation=%d: self.best_fitness=%f, self.best_individual:%s"
            #             % (generation, self.best_fitness, self.best_individual))
            logger.info("generation=%d: self.best_fitness=%f" % (generation, self.best_fitness))
        return self.best_individual, self.best_fitness, self.best_fitness_history, self.best_individual_history


# ============================================= 主函数 ====================================
# GA-PS 核心算法 -- 双波束
def ga_ps_beam_2(phase1, phase2, bit_num):
    # 1.PS方式初始化结果
    phase_mix, phaseBit_mix, phaseBitDeg_mix = psm_beam_2(phase1, phase2, bit_num)
    # 2.遗传算法(GA)寻找最优的phaseBit_mix
    ga = RISGeneticAlgorithmPS(bit_num, 2, 50, 150, 10, 0.1)
    best_individual, best_fitness, best_fitness_history, best_individual_history = ga.run(phase_mix)
    # logger.info("best_individual:%s" % (best_individual))
    logger.info("Best Fitness=%f" % (best_fitness))
    #
    return best_individual, best_fitness, best_fitness_history, best_individual_history


# GA-PS 核心算法 -- 四波束
def ga_ps_beam_4(phase1, phase2, phase3, phase4, bit_num):
    # 1.PS方式初始化结果
    phase_mix, phaseBit_mix, phaseBitDeg_mix = psm_beam_4(phase1, phase2, phase3, phase4, bit_num)
    # 2.遗传算法(GA)寻找最优的phaseBit_mix
    ga = RISGeneticAlgorithmPS(bit_num, 4, 50, 150, 10, 0.1)
    best_individual, best_fitness, best_fitness_history, best_individual_history = ga.run(phase_mix)
    # logger.info("best_individual:%s" % (best_individual))
    logger.info("Best Fitness=%f" % (best_fitness))
    #
    return best_individual, best_fitness, best_fitness_history, best_individual_history


# GA-PS 核心算法 -- 八波束
def ga_ps_beam_8(phase1, phase2, phase3, phase4, phase5, phase6, phase7, phase8, bit_num):
    # 1.PS方式初始化结果
    phase_mix, phaseBit_mix, phaseBitDeg_mix = psm_beam_n(
        [phase1, phase2, phase3, phase4, phase5, phase6, phase7, phase8], bit_num)
    # 2.遗传算法(GA)寻找最优的phaseBit_mix
    ga = RISGeneticAlgorithmPS(bit_num, 8, 50, 150, 10, 0.1)
    best_individual, best_fitness, best_fitness_history, best_individual_history = ga.run(phase_mix)
    # logger.info("best_individual:%s" % (best_individual))
    logger.info("Best Fitness=%f" % (best_fitness))
    #
    return best_individual, best_fitness, best_fitness_history, best_individual_history


# 几何分区法 -- 双波束
def main_multi_beam_2(theta1, phi1, theta2, phi2, path_pre, bit_num):
    logger.info("main_multi_beam_2: bit_num=%d, path_pre=%s, " % (bit_num, path_pre))
    logger.info("main_multi_beam_2: theta1=%d, phi1=%d, theta2=%d, phi2=%d, " % (theta1, phi1, theta2, phi2))
    # 目前只支持2bit
    if bit_num > 2:
        logger.error("main_multi_beam_2: bit_num bigger than 2.")
        return
    phase1, phaseBit1, pattern1 = point_2_phi_pattern(theta1, phi1, bit_num)
    phase2, phaseBit2, pattern2 = point_2_phi_pattern(theta2, phi2, bit_num)
    # 确保 phase1 和 phase2 具有相同的形状
    assert phaseBit1.shape == phaseBit2.shape, "phase1 和 phase2 必须具有相同的形状"
    # GA-PS
    phase_mix, best_fitness, best_fitness_history, best_individual_history = ga_ps_beam_2(phase1, phase2, bit_num)
    # 相位转换 X bit
    phaseBit_mix, phaseBitDeg_mix = phase_2_bit(phase_mix, bit_num)
    # 计算phase_mix的方向图
    phaseBit_mix = np.deg2rad(phaseBitDeg_mix)
    patternBit_mix = phase_2_pattern(phaseBit_mix)
    #
    # 保存结果
    logger.info("save GA-PS multi-beam 2 result...")
    patternBit_mix_xyz, x, y, z = phase_2_pattern_xyz(phaseBit_mix)
    # 保存图片
    save_img(path_pre + "phase1.jpg", phase1)
    save_img(path_pre + "phase2.jpg", phase2)
    save_img(path_pre + "phaseBit1.jpg", phaseBit1)
    save_img(path_pre + "phaseBit2.jpg", phaseBit2)
    save_img(path_pre + "pattern1.jpg", pattern1)
    save_img(path_pre + "pattern2.jpg", pattern2)
    save_img(path_pre + "phaseBit_mix.jpg", phaseBit_mix)         # 几何分区法 -- 结果码阵
    save_img(path_pre + "patternBit_mix.jpg", patternBit_mix)     # 几何分区法 -- 结果码阵方向图
    save_img_xyz(path_pre + "patternBit_mix_xyz.jpg", np.abs(patternBit_mix_xyz), x, y)
    # 保存相位结果
    save_csv(phase1, path_pre + "phase1.csv")
    save_csv(phase2, path_pre + "phase2.csv")
    save_csv(phaseBit1, path_pre + "phaseBit1.csv")
    save_csv(phaseBit2, path_pre + "phaseBit2.csv")
    save_csv(phaseBit_mix, path_pre + "phaseBit_mix.csv")
    # 保存遗传算法优化结果
    save_line_chart(path_pre + "best_fitness_history.jpg", best_fitness_history,
                    "best_fitness_history", "iteration", "best_fitness", "fitness")
    save_csv([[item] for item in best_fitness_history], path_pre + "best_fitness_history.csv")
    save_csv(best_individual_history, path_pre + "best_individual_history.csv")


# 几何分区法 -- 四波束
def main_multi_beam_4(theta1, phi1, theta2, phi2, theta3, phi3, theta4, phi4, path_pre, bit_num):
    logger.info("main_multi_beam_4: bit_num=%d, path_pre=%s, " % (bit_num, path_pre))
    logger.info("main_multi_beam_4: theta1=%d, phi1=%d, theta2=%d, phi2=%d, theta3=%d, phi3=%d, theta4=%d, phi4=%d"
                % (theta1, phi1, theta2, phi2, theta3, phi3, theta4, phi4))
    # 目前只支持2bit
    if bit_num > 2:
        logger.error("main_multi_beam_N: bit_num bigger than 2.")
        return
    phase1, phaseBit1, pattern1 = point_2_phi_pattern(theta1, phi1, bit_num)
    phase2, phaseBit2, pattern2 = point_2_phi_pattern(theta2, phi2, bit_num)
    phase3, phaseBit3, pattern3 = point_2_phi_pattern(theta3, phi3, bit_num)
    phase4, phaseBit4, pattern4 = point_2_phi_pattern(theta4, phi4, bit_num)
    # 确保所有数组具有相同的形状
    assert phaseBit1.shape == phaseBit2.shape == phaseBit3.shape == phaseBit4.shape, "所有数组必须具有相同的形状"
    # GA-PS
    phase_mix, best_fitness, best_fitness_history, best_individual_history \
        = ga_ps_beam_4(phase1, phase2, phase3, phase4, bit_num)
    # 相位转换 X bit
    phaseBit_mix, phaseBitDeg_mix = phase_2_bit(phase_mix, bit_num)
    # 计算phase_mix的方向图
    phaseBit_mix = np.deg2rad(phaseBitDeg_mix)
    # 计算phase_mix
    patternBit_mix = phase_2_pattern(phaseBit_mix)
    #
    # 保存结果
    logger.info("save GA-PS multi-beam 4 result...")
    patternBit_mix_xyz, x, y, z = phase_2_pattern_xyz(phaseBit_mix)
    # 保存图片
    save_img(path_pre + "phase1.jpg", phase1)
    save_img(path_pre + "phase2.jpg", phase2)
    save_img(path_pre + "phase3.jpg", phase3)
    save_img(path_pre + "phase4.jpg", phase4)
    save_img(path_pre + "phaseBit1.jpg", phaseBit1)
    save_img(path_pre + "phaseBit2.jpg", phaseBit2)
    save_img(path_pre + "phaseBit3.jpg", phaseBit3)
    save_img(path_pre + "phaseBit4.jpg", phaseBit4)
    save_img(path_pre + "pattern1.jpg", pattern1)
    save_img(path_pre + "pattern2.jpg", pattern2)
    save_img(path_pre + "pattern3.jpg", pattern3)
    save_img(path_pre + "pattern4.jpg", pattern4)
    save_img(path_pre + "phase_mix.jpg", phaseBit_mix)       # 几何分区法 -- 结果码阵
    save_img(path_pre + "pattern_mix.jpg", patternBit_mix)   # 几何分区法 -- 结果码阵方向图
    save_img_xyz(path_pre + "patternBit_mix_xyz.jpg", np.abs(patternBit_mix_xyz), x, y)
    # 保存相位结果
    save_csv(phase1, path_pre + "phase1.csv")
    save_csv(phase2, path_pre + "phase2.csv")
    save_csv(phase3, path_pre + "phase3.csv")
    save_csv(phase4, path_pre + "phase4.csv")
    save_csv(phaseBit1, path_pre + "phaseBit1.csv")
    save_csv(phaseBit2, path_pre + "phaseBit2.csv")
    save_csv(phaseBit3, path_pre + "phaseBit3.csv")
    save_csv(phaseBit4, path_pre + "phaseBit4.csv")
    save_csv(phaseBit_mix, path_pre + "phase_mix.csv")
    # 保存退火算法优化结果
    save_line_chart(path_pre + "best_fitness_history.jpg", best_fitness_history,
                    "best_fitness_history", "iteration", "best_fitness", "fitness")
    save_csv([[item] for item in best_fitness_history], path_pre + "best_fitness_history.csv")
    save_csv(best_individual_history, path_pre + "best_individual_history.csv")


def main_multi_beam_8(theta1, phi1, theta2, phi2, theta3, phi3, theta4, phi4,
                      theta5, phi5, theta6, phi6, theta7, phi7, theta8, phi8,
                      path_pre, bit_num):
    logger.info("main_multi_beam_8: bit_num=%d, path_pre=%s, " % (bit_num, path_pre))
    logger.info("main_multi_beam_8: theta1=%d, phi1=%d, theta2=%d, phi2=%d, theta3=%d, phi3=%d, theta4=%d, phi4=%d, "
                "theta5=%d, phi5=%d, theta6=%d, phi6=%d, theta7=%d, phi7=%d, theta8=%d, phi8=%d"
                % (theta1, phi1, theta2, phi2, theta3, phi3, theta4, phi4,
                   theta5, phi5, theta6, phi6, theta7, phi7, theta8, phi8))
    # 目前只支持2bit
    if bit_num > 2:
        logger.error("main_multi_beam_8: bit_num bigger than 2.")
        return
    # 获取所有的 phaseBit 变量
    phase1, phaseBit1, pattern1 = point_2_phi_pattern(theta1, phi1, bit_num)
    phase2, phaseBit2, pattern2 = point_2_phi_pattern(theta2, phi2, bit_num)
    phase3, phaseBit3, pattern3 = point_2_phi_pattern(theta3, phi3, bit_num)
    phase4, phaseBit4, pattern4 = point_2_phi_pattern(theta4, phi4, bit_num)
    phase5, phaseBit5, pattern5 = point_2_phi_pattern(theta5, phi5, bit_num)
    phase6, phaseBit6, pattern6 = point_2_phi_pattern(theta6, phi6, bit_num)
    phase7, phaseBit7, pattern7 = point_2_phi_pattern(theta7, phi7, bit_num)
    phase8, phaseBit8, pattern8 = point_2_phi_pattern(theta8, phi8, bit_num)
    # 确保所有数组具有相同的形状
    assert phaseBit1.shape == phaseBit2.shape == phaseBit3.shape == phaseBit4.shape == \
           phaseBit5.shape == phaseBit6.shape == phaseBit7.shape == phaseBit8.shape, "所有数组必须具有相同的形状"
    # GA-PS
    phase_mix, best_fitness, best_fitness_history, best_individual_history \
        = ga_ps_beam_8(phase1, phase2, phase3, phase4, phase5, phase6, phase7, phase8, bit_num)
    # 相位转换 X bit
    phaseBit_mix, phaseBitDeg_mix = phase_2_bit(phase_mix, bit_num)
    # 计算phase_mix的方向图
    phaseBit_mix = np.deg2rad(phaseBitDeg_mix)
    # 计算phase_mix
    patternBit_mix = phase_2_pattern(phaseBit_mix)
    # 保存结果
    logger.info("save GA-PS multi-beam 8 result...")
    patternBit_mix_xyz, x, y, z = phase_2_pattern_xyz(phaseBit_mix)
    # 保存图片
    save_img(path_pre + "phase1.jpg", phase1)
    save_img(path_pre + "phase2.jpg", phase2)
    save_img(path_pre + "phase3.jpg", phase3)
    save_img(path_pre + "phase4.jpg", phase4)
    save_img(path_pre + "phase5.jpg", phase5)
    save_img(path_pre + "phase6.jpg", phase6)
    save_img(path_pre + "phase7.jpg", phase7)
    save_img(path_pre + "phase8.jpg", phase8)
    save_img(path_pre + "phaseBit1.jpg", phaseBit1)
    save_img(path_pre + "phaseBit2.jpg", phaseBit2)
    save_img(path_pre + "phaseBit3.jpg", phaseBit3)
    save_img(path_pre + "phaseBit4.jpg", phaseBit4)
    save_img(path_pre + "phaseBit5.jpg", phaseBit5)
    save_img(path_pre + "phaseBit6.jpg", phaseBit6)
    save_img(path_pre + "phaseBit7.jpg", phaseBit7)
    save_img(path_pre + "phaseBit8.jpg", phaseBit8)
    save_img(path_pre + "pattern1.jpg", pattern1)
    save_img(path_pre + "pattern2.jpg", pattern2)
    save_img(path_pre + "pattern3.jpg", pattern3)
    save_img(path_pre + "pattern4.jpg", pattern4)
    save_img(path_pre + "pattern5.jpg", pattern5)
    save_img(path_pre + "pattern6.jpg", pattern6)
    save_img(path_pre + "pattern7.jpg", pattern7)
    save_img(path_pre + "pattern8.jpg", pattern8)
    save_img(path_pre + "phaseBit_mix.jpg", phaseBit_mix)  # 几何分区法 -- 结果码阵
    save_img(path_pre + "patternBit_mix.jpg", patternBit_mix)  # 几何分区法 -- 结果码阵方向图
    save_img_xyz(path_pre + "patternBit_mix_xyz.jpg", np.abs(patternBit_mix_xyz), x, y)
    # 保存相位结果
    save_csv(phase1, path_pre + "phase1.csv")
    save_csv(phase2, path_pre + "phase2.csv")
    save_csv(phase3, path_pre + "phase3.csv")
    save_csv(phase4, path_pre + "phase4.csv")
    save_csv(phase5, path_pre + "phase5.csv")
    save_csv(phase6, path_pre + "phase6.csv")
    save_csv(phase7, path_pre + "phase7.csv")
    save_csv(phase8, path_pre + "phase8.csv")
    save_csv(phaseBit1, path_pre + "phaseBit1.csv")
    save_csv(phaseBit2, path_pre + "phaseBit2.csv")
    save_csv(phaseBit3, path_pre + "phaseBit3.csv")
    save_csv(phaseBit4, path_pre + "phaseBit4.csv")
    save_csv(phaseBit5, path_pre + "phaseBit5.csv")
    save_csv(phaseBit6, path_pre + "phaseBit6.csv")
    save_csv(phaseBit7, path_pre + "phaseBit7.csv")
    save_csv(phaseBit8, path_pre + "phaseBit8.csv")
    save_csv(phaseBit_mix, path_pre + "phaseBit_mix.csv")
    # 保存退火算法优化结果
    save_line_chart(path_pre + "best_fitness_history.jpg", best_fitness_history,
                    "best_fitness_history", "iteration", "best_fitness", "fitness")
    save_csv([[item] for item in best_fitness_history], path_pre + "best_fitness_history.csv")
    save_csv(best_individual_history, path_pre + "best_individual_history.csv")


# ======================================================= main 主方法 ===============================================
def main_multi_ga_ps():
    # 基于GA-PS的方法: 主函数
    # 测试方法
    #
    # 几何分区法: 主函数
    # main_multi_beam_2(30, 0, 30, 90,
    #                   "../files/multi-beam/1bit/GA-PS/2-(30,0,30,90)/", 1)
    # main_multi_beam_2(30, 0, 30, 180,
    #                   "../files/multi-beam/1bit/GA-PS/2-(30,0,30,180)/", 1)
    # main_multi_beam_4(30, 0, 30, 60, 30, 120, 30, 180,
    #                   "../files/multi-beam/1bit/GA-PS/4-(30,0,30,60,30,120,30,180)/", 1)
    # main_multi_beam_4(30, 0, 30, 90, 30, 180, 30, 270,
    #                   "../files/multi-beam/1bit/GA-PS/4-(30,0,30,90,30,180,30,270)/", 1)
    # main_multi_beam_8(30, 0, 30, 45, 30, 90, 30, 135, 30, 180, 30, 225, 30, 270, 30, 315,
    #                   "../files/multi-beam/1bit/GA-PS/8-(30,45step)/", 1)
    #
    main_multi_beam_2(30, 0, 30, 90,
                      "../files/multi-beam/2bit/GA-PS/2-(30,0,30,90)/", 2)
    main_multi_beam_2(30, 0, 30, 180,
                      "../files/multi-beam/2bit/GA-PS/2-(30,0,30,180)/", 2)
    main_multi_beam_4(30, 0, 30, 60, 30, 120, 30, 180,
                      "../files/multi-beam/2bit/GA-PS/4-(30,0,30,60,30,120,30,180)/", 2)
    main_multi_beam_4(30, 0, 30, 90, 30, 180, 30, 270,
                      "../files/multi-beam/2bit/GA-PS/4-(30,0,30,90,30,180,30,270)/", 2)
    main_multi_beam_8(30, 0, 30, 45, 30, 90, 30, 135, 30, 180, 30, 225, 30, 270, 30, 315,
                      "../files/multi-beam/2bit/GA-PS/8-(30,45step)/", 2)





if __name__ == '__main__':
    logger.info("1bit-RIS-multi-beam-PS: GA based on PS method")
    main_multi_ga_ps()